SKen: A Statistical Test for Removing Outliers in Optical Flow - A 3D Reconstruction Case

Samuel Macedo, Luis Vasconcelos, Vinicius Cesar, Saulo Pessoa, Judith Kelner

2014

Abstract

The 3D reconstruction can be employed in several areas such as markerless augmented reality, manipulation of interactive virtual objects and to deal with the occlusion of virtual objects by real ones. However, many improvements into the 3D reconstruction pipeline in order to increase its efficiency may still be done. In such context, this paper proposes a filter for optimizing a 3D reconstruction pipeline. It is presented the SKen technique, a statistical hypothesis test that classifies the features by checking the smoothness of its trajectory. Although it was not mathematically proven that inliers features performed smooth camera paths, this work shows some evidence of a relationship between smoothness and inliers. By removing features that did not present smooth paths, the quality of the 3D reconstruction was enhanced.

References

  1. Barbosa, R. L. (2006). Caminhamento Fotogramtrico Utilizando o Fluxo ptico Filtrado. PhD thesis, UNESP.
  2. Bolfarine, H. and Bussab, W. O. (2005). Elementos de Amostragem. Editora Edgard Blucher, So Paulo.
  3. Bouguet, J. Y. (2000). Pyramidal implementation of the lucas kanade feature tracker: Description of the algorithm. Technical report, Intel Corporation.
  4. Bussab, W. and Morettin, P. (2002). Estatstica Bsica. Editora Saraiva.
  5. Choi, J. and Medioni, G. (2009). Starsac: Stable random sample consensus for parameter estimation. IEEE International Conference on Computer Vision and Pattern Recognition, pages 675-682.
  6. Farias, T. S. M. C. (2012). Metodologia para Reconstruo 3D Baseada em Imagens. PhD thesis, UFPE.
  7. Fischler, M. A. and Bolles, R. C. (1987). Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Morgan Kaufmann Publishers Inc.
  8. Furukawa, Y. and Ponce, J. (2010). Accurate, dense and robust multiview stereopsis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8):1362- 1376.
  9. Hartley, R. I. and Zisserman, A. (2004). Multiple View Geometry in Computer. 2nd ed. Cambridge University Press, ISBN: 0521540518.
  10. Hming, K. and Peters, G. (2010). The structure from motion reconstruction pipeline: a survey with focus on short image. Kybernetika.
  11. Hollander, M. and Wolfe, D. A. (1999). Nonparametric Statistical Methods, 2nd Edition. Wiley-Interscience, 2 edition.
  12. James, B. R. (2002). Probabilidade: Um curso em nvel intermedirio. IMPA, Rio de Janeiro.
  13. Lucas, B. D. and Kanade, T. (1981). An iterative image registration technique with an application to stereo vision (ijcai). In Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI 7881), pages 674-679.
  14. Nistér, D. (2003). Preemptive RANSAC for Live Structure and Motion Estimation. In Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2, ICCV 7803, pages 199-, Washington, DC, USA. IEEE Computer Society.
  15. Pollefeys, M. (1999). Self-calibration and metric 3D reconstruction uncalibrated image sequeces. PhD thesis, ESAT-PSI.
  16. Rodehorst, V. and Hellwich, O. (2006). Genetic algorithm sample consensus (gasac) - a parallel strategy for robust parameter estimation. In Computer Vision and Pattern Recognition Workshop, 2006. CVPRW 7806. Conference on, page 103.
  17. Rousseeuw, P. and Leroy, A. (1987). Robust Regression and Outlier Detection. John Wiley.
  18. Shi, J. and Tomasi, C. (1994). Good features to track. In 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'94), pages 593 - 600.
  19. Torr, P. H. S. and Zisserman, A. (2000). MLESAC: A New Robust Estimator with Application to Estimating Image Geometry. Computer Vision and Image Understanding, 78:2000.
Download


Paper Citation


in Harvard Style

Macedo S., Vasconcelos L., Cesar V., Pessoa S. and Kelner J. (2014). SKen: A Statistical Test for Removing Outliers in Optical Flow - A 3D Reconstruction Case . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 202-209. DOI: 10.5220/0004748802020209


in Bibtex Style

@conference{visapp14,
author={Samuel Macedo and Luis Vasconcelos and Vinicius Cesar and Saulo Pessoa and Judith Kelner},
title={SKen: A Statistical Test for Removing Outliers in Optical Flow - A 3D Reconstruction Case},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={202-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004748802020209},
isbn={978-989-758-003-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - SKen: A Statistical Test for Removing Outliers in Optical Flow - A 3D Reconstruction Case
SN - 978-989-758-003-1
AU - Macedo S.
AU - Vasconcelos L.
AU - Cesar V.
AU - Pessoa S.
AU - Kelner J.
PY - 2014
SP - 202
EP - 209
DO - 10.5220/0004748802020209